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US20110128282A1 - Method for Generating the Depth of a Stereo Image - Google Patents

Method for Generating the Depth of a Stereo Image Download PDF

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Publication number
US20110128282A1
US20110128282A1 US12/780,074 US78007410A US2011128282A1 US 20110128282 A1 US20110128282 A1 US 20110128282A1 US 78007410 A US78007410 A US 78007410A US 2011128282 A1 US2011128282 A1 US 2011128282A1
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Prior art keywords
image
pixels
paths
depths
dynamic programming
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US12/780,074
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Inventor
Chin-Yuan Wang
Chia-Hang Ho
Chun-Te Wu
Wei-Jia Huang
Kai-Che Liu
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Industrial Technology Research Institute ITRI
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Industrial Technology Research Institute ITRI
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Assigned to INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE reassignment INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HO, CHIA-HANG, HUANG, WEI-JIA, LIU, KAI-CHE, WANG, CHIN-YUAN, WU, CHUN-TE
Publication of US20110128282A1 publication Critical patent/US20110128282A1/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/593—Depth or shape recovery from multiple images from stereo images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/97—Determining parameters from multiple pictures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images

Definitions

  • the disclosure relates in general to a method for generating image depth of a stereo image, and more particularly to a method for generating image depth of a stereo image through multiple paths with greater gradient.
  • the belief propagation algorithm and the dynamic programming algorithm are commonly used in the stereo matching technology. Let the technology disclosed in United States Patent No. 2009/0129667 be taken for example. Despite the image depths obtained by the belief propagation algorithm is more accurate, a larger memory and a larger amount of computing time are required. Let the technology disclosed in U.S. Pat. No. 7,570,804 be taken for example.
  • the dynamic programming algorithm has the advantages of requiring smaller memory and smaller amount of computing time. In the conventional method which computes the image depth by the dynamic programming algorithm, the entire scan line (single column or single row) is optimized.
  • the disclosure is directed to a method for generating image depth of a stereo image.
  • a method for generating image depth of a stereo image includes the following steps. Firstly, a stereo image is received. Next, a number of paths with greater gradient are searched in the stereo image. Then, the image depths of a number of first pixels in the paths are generated. After that, the image depths of a number of second pixels not in the paths are generated according to the image depths of the first pixels.
  • FIG. 1 is a flowchart of a method for generating image depth of a stereo image according to an embodiment of the disclosure
  • FIGS. 2A ⁇ 2D is schematic diagrams which show an example of obtaining a path by the greedy algorithm
  • FIG. 3 is a diagram showing multiple paths
  • FIG. 4 is a block diagram of a system used for performing the method for generating image depth of a stereo image of FIG. 1 .
  • FIG. 1 a flowchart of a method for generating image depth of a stereo image according to an embodiment of the disclosure is shown.
  • the method disclosed in the present embodiment of the disclosure includes the following steps. Firstly, the method begins at step 102 , a stereo image is received. Next, the method proceeds to step 104 , multiple paths with greater gradient are searched in the stereo image. Then, the method proceeds to step 106 , multiple image depths of the first pixels in the paths are generated. After that, the method proceeds to step 108 , according to the image depths of the first pixels, multiple image depths of the second pixels not in the paths are generated.
  • the multiple paths with greater gradient preferably are paths with greater color change.
  • the depths are more likely to be wrongly calculated in the region with smaller color change, and the calculation method using one row or one column as a unit tends to produce streak noise in the depth chart.
  • paths with greater gradient such as paths with greater color change are searched in the image first, and then the depths of the image are calculated afterwards. After that, other pixel depths of the image are calculated by using other algorithms.
  • the depths of the pixels obtained in the paths with greater color change have higher accuracy.
  • the accuracy of the depths of image is increased if multiple depths of the pixels in paths with higher accuracy are obtained first and then the depths of the pixels not in the paths are obtained next.
  • the occurrence of streak noise in the depths is effectively reduced, and the quality of the three-dimensional image generated according to the depth is increased.
  • the stereo image being received such as includes a left-eye two-dimensional image and a right-eye two-dimensional image.
  • multiple paths with greater gradient can be searched according to one of the left-eye two-dimensional image and the right-eye two-dimensional image.
  • the paths with greater gradient can be obtained by using the greedy algorithm or the dynamic programming algorithm, but the disclosure is not limited thereto.
  • FIGS. 2A ⁇ 2D an example of obtaining a path by the greedy algorithm is shown.
  • the starting point of the path be pixel P 1 .
  • the three pixels adjacent to the pixel P 1 are candidate points as indicated by arrow.
  • the pixel whose color or grey value differs with the pixel P 1 most is selected as the second pixel in the path.
  • the selected pixel P 2 is indicated in FIG. 2B .
  • the pixel whose color or grey value differs with the pixel P 2 most is selected as the third pixel in the path as indicated in FIG. 2C .
  • the above step is repeated so as to obtain n points in the path as indicated in FIG. 2D .
  • a path L 1 composed of P 1 , P 2 . . . Pn is obtained.
  • FIG. 3 Another path L 2 as indicated in FIG. 3 is obtained by repeating FIGS. 2A ⁇ 2D .
  • Other paths (illustrated in the diagram) are obtained by repeating FIGS. 2A ⁇ 2D .
  • the energy function e 1 of each pixel in an image is defined as:
  • I denotes the brightness value of the pixel.
  • the path s y is defined as:
  • (j, y (j)) denotes the coordinates of pixels in the paths, and m denotes the number of pixels included in one row of an image.
  • the difference in the y coordinate is within one pixel.
  • the path to be searched in the present embodiment of the disclosure is the path with the smallest sum of the energy of all pixels, and must be conformed to the following expression of s*:
  • an accumulative energy function M (i, j) is defined as:
  • M ( i,j ) e ( i,j )+max( M ( i ⁇ 1 ,j ⁇ 1), M ( i ⁇ 1 ,j ), M ( i ⁇ 1 ,j+ 1))
  • the maximum value of M (i, j) can be searched by using the dynamic programming algorithm, so as to obtain the entire path with largest energy by inference.
  • the image depths of the first pixels in the paths are preferably obtained by the dynamic programming algorithm.
  • the energy function of the dynamic programming algorithm such as includes a matching cost function and a penalty function.
  • the present embodiment of the disclosure uses the following energy function:
  • E path ⁇ ( d ⁇ ( x , y ) ) ⁇ ( x , y ) ⁇ S * ⁇ C ⁇ ( x , y , d ⁇ ( x , y ) ) + ⁇ ( x , y ) ⁇ S * ⁇ ⁇ ⁇ ( x , y ) ⁇ ⁇ ⁇ ( d ⁇ ( x , y ) ) - d ⁇ ( x + 1 , y x + 1 ) )
  • C(x, y, d(x,y)) denotes the matching cost when the disparity of the pixel (x,y) equals d(x,y).
  • ⁇ (x,y), ⁇ (d) are penalty functions arbitrarily defined.
  • I Left (x, y) and I Right (x, y) respectively denote the brightness values of the left-eye image pixel (x, y) and the right-eye image pixel (x, y), k is a given constant.
  • E path is minimized by using the dynamic programming algorithm so as to obtain the image depths corresponding to all pixels in the path s*.
  • multiple image depths of the second pixels not in the paths can be generated by using the bilateral filter or by using the dynamic programming algorithm.
  • the second pixel is such as the pixel P 1 ′ of FIG. 3 .
  • the method of generating the image depths by using the bilateral filter is disclosed below.
  • the bilateral filter is a low-pass filter which maintains the details of image edge.
  • the bilateral filter is used for generating the depth values of the pixels not in the paths of the depth chart so as to produce a high-quality depth chart.
  • p denotes the pixel to which filter processing is performed
  • ⁇ denotes a mask range centered at p
  • I pf denotes the color of the filtered pixel
  • I p and I q respectively denote the colors of pixels p and q
  • Gs and Gr denote two low-pass filters, the former functions in the pixel space, and the latter functions in the color space.
  • the present embodiment of the disclosure uses “Real-Time Edge-Aware Image Processing With The Bilateral Grid” disclosed by Chen, J., Paris, S., and Durand, F. 2007 as well as the method of bilateral grid disclosed in ACM SIGGRAPH 2007 (San Diego, Calif., Aug. 5-9, 2007).
  • Bilateral grid is a data structure which maps a two-dimensional image onto a three-dimensional space grid, wherein the mapping function is expressed as:
  • r and s denote two adjustable parameters; (u, v) denotes the coordinates of pixels in a two-dimensional image; I (u, v) denotes the brightness value of pixel (u, v); (x, y, z) denotes the pixel coordinate after the pixel (u, v) is mapped into the three-dimensional space grid.
  • the mask range must be large enough such as covers 1/36 ⁇ 1 ⁇ 4 of the image.
  • the I (u, v) of the mapping function uses the brightness value of the source image, but the values stored in the grid are changed to (d, n), wherein d denotes the sum of depth estimates of all pixels mapped into the grid, and n also denotes the number of pixels mapped into the grid.
  • the unknown depths of remaining multiple second pixels can be obtained by using the dynamic programming algorithm.
  • the unknown depths of remaining multiple second pixels can be compensated by scan line optimization utilized in conventional method.
  • the depths of multiple second pixels not in the paths can be obtained according to the depths of multiple first pixels in the paths obtained in the step 106 .
  • Multiple image depths of the second pixels are calculated along the row direction or along the column direction.
  • bilateral filtering is parallelly performing by dividing the stereo image into a number of blocks and using each block which is treated as an operation unit to save computing time and parallelly doing the operation on each block.
  • the disclosure provides a system used for performing the method for generating image depth of a stereo image of FIG. 1 , wherein the block diagram of the system is indicated in FIG. 4 .
  • the system 400 includes an image processing unit 402 and a storage unit 404 .
  • the image processing unit 402 is for receiving the stereo image Im and performing the steps 102 ⁇ 108 of FIG. 1
  • the storage unit 404 is for storing the image depths of the stereo image Im and the first pixels and the second pixels.
  • the method for generating image depth of a stereo image disclosed in the above embodiments of the disclosure increases the accuracy of image depth and is conducive to enhancing the quality of subsequent three-dimensional image.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Processing Or Creating Images (AREA)
  • Image Processing (AREA)
  • Image Generation (AREA)
  • Testing, Inspecting, Measuring Of Stereoscopic Televisions And Televisions (AREA)
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US20110038529A1 (en) * 2009-08-12 2011-02-17 Hitachi, Ltd. Image processing apparatus and image processing method
US20120092462A1 (en) * 2010-10-14 2012-04-19 Altek Corporation Method and apparatus for generating image with shallow depth of field
WO2013075611A1 (zh) * 2011-11-23 2013-05-30 华为技术有限公司 一种深度图像滤波方法、获取深度图像滤波阈值的方法和装置
US20130258064A1 (en) * 2012-04-03 2013-10-03 Samsung Techwin Co., Ltd. Apparatus and method for reconstructing high density three-dimensional image
US9007441B2 (en) 2011-08-04 2015-04-14 Semiconductor Components Industries, Llc Method of depth-based imaging using an automatic trilateral filter for 3D stereo imagers
US9047656B2 (en) * 2009-01-20 2015-06-02 Entropic Communications, Inc. Image processing using a bilateral grid
US9070196B2 (en) 2012-02-27 2015-06-30 Samsung Electronics Co., Ltd. Apparatus and method for estimating disparity using visibility energy model
WO2020113824A1 (zh) * 2018-12-04 2020-06-11 深圳市华星光电半导体显示技术有限公司 图像处理方法
US10992873B2 (en) * 2019-01-18 2021-04-27 Qualcomm Incorporated Systems and methods for color matching for realistic flash images

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TWI456526B (zh) * 2011-11-03 2014-10-11 Au Optronics Corp 多視角立體影像產生方法及採用此方法之多視角立體影像產生裝置
KR101888969B1 (ko) * 2012-09-26 2018-09-20 엘지이노텍 주식회사 영상특성을 이용한 스테레오 매칭장치
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WO2020113824A1 (zh) * 2018-12-04 2020-06-11 深圳市华星光电半导体显示技术有限公司 图像处理方法
US10992873B2 (en) * 2019-01-18 2021-04-27 Qualcomm Incorporated Systems and methods for color matching for realistic flash images

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